Reply is the place to meet an incredible variety of enthusiastic, passionate, ideas-driven people, who want to make a difference and an impact.Would you like to know more?
We investigated the link between the main Marketing trends and key enabling technologies, conducting a research through Reply‘s SONAR Trend Platform. This enabled us to create an overview and mapping of relevant trends based on their occurrence within expert media articles, mass media, patents and scientific publications.
The “Strategic Framework for Artificial Intelligence in Marketing” is a model for understanding how
advanced technologies are inciting transformation across a spectrum of customer experiences – from
Marketing Research, to Marketing Strategy, and Marketing Action.
"The cycle views strategic planning as a circular process. In this context, the execution of marketing
actions will feed back to marketing research as market data, which constitutes a continuous cycle for
Marketing Research – Strategy – Action."
Source: Huang, MH., Rust, R.T. November 2020. "A strategic framework for artificial intelligence in marketing.", J. of the Acad. Mark. Sci.
Reply analysts identified trends relevant to AI and CX within Marketing Research, Strategy, and Action, as well as the underlying enabling technologies. By using SONAR Trend Platform, we could create an overview and mapping of relevant trends related to Artificial Intelligence and Customer Experience.
For decades, classic market research focus groups proved the best way to find out
how consumers felt. The advent of Web 2.0 analytics created means of listening
to consumer reactions at scale. But with human language adapting, evolving and
changing constantly,being able to judge sentiment remained surface level. By
training human text, AI can now pick nuances in consumer sentiment helping the
industry reach for the holy grail – understanding consumer emotions at scale.
Before the advent of data analytics into the Marketing process, most consumer
journey planning was ideal scenario guess work. What was lacking was in the
field testing to validate best case planning. The current status quo is modern
consumer journeys that come with KPIs attached, ready to be tracked and
optimized based on data. AI will soon take over driving this area of research
forward into real time, helping collect, consolidate and optimize Marketing
touchpoints to improve the consumer experience.
Image recognition algorithms are allowing machines to interpret what they “see” in images or videos. Recognizing what is in millions of pictures and categorizing it, is often referred to as “image labelling” or “image classification”. These ML algorithms require training on content in order to learn all the possible elements that will lead to their labelling. This is helping marketers to save time and resources because, instead of searching hundreds of posts through intuition, AI can pull images or videos out of millions of examples and organize them based on specific trends based in mood, color, scenery, or the objects found in the images, all organized instantly.
Content marketers are leveraging machine learning algorithms to discover the types of content or offer that maximizes objective functions such as e-mail form submissions, registrations, or a purchase transaction in order to replicate them. Moreover, applying Machine Learning Operations to content optimization and testing is maximizing the potential to resonate with consumers enabling organizations to map content against their audiences and provide the most relevant content based on where each person is within the journey.
Customer segmentation has been used for years across industries to help reduce waste in Marketing campaigns and help in other tasks such as product recommendations, pricing, and up-selling strategies. It is now a building block for companies to optimize their Customer Experience as it helps tailor it precisely to the different customer segments and their specific needs, enabling brands target individuals more precisely, using unique messages and offerings across all touchpoints in the consumption journey and the buying occasions.
For some years the Marketing Effect Modelling has led the way in analyzing the impact of media and communications on a brand and companies bottom line. New AI tools are helping innovate Marketing Effectiveness Modelling – capturing social and search data and building algorithmic proxies to predict sales performance. Our prediction is of a near future where Marketing planning is replaced by reactive optimization – using live modelling to course correct and improve advertising effectiveness in real time.
Chatbots and virtual assistants are lucrative AI technologies for the Marketing industry. When done right, these technologies offer the opportunity for brands and businesses to offer one-to-one customer service at scale. The next iteration of this field of Marketing action will focus not just on the technological capability, but also with the data gathering opportunity. Brands and companies that can not only deploy chatbots across the Customer Experience, but also leverage the data they produce, will be able to stay a step ahead of the industry competition in the future.
Enhancing Brand Loyalty is a step beyond mere consumer satisfaction - ensuring sustained revenue generation and perhaps even recruiting more consumers through brand affinity. To get to the heart of what makes consumers feel loyal to a brand, AI can be employed through Marketing Research and Strategy to get to the core ofthese drivers and offer meaningful experiences, whether through promotions or personalized engagement.
In today's age, culture and content is now being dictated by personalized algorithms – if you like that, you might like this. Marketing and e-commerce still have some way to go, however. With changes to platform data and Cookie policies, too many brands still follow customers across the interest with products and services that they have already purchased or have no interest in. As other industries pave the way in customer recommendation systems the pressure will be on Marketing departments to improve their own algorithms or risk being shunned by customers.
AI integration into existing Marketing and businesses practice opens up new avenues for consumer data gathering, helping brands to gain unique competitive edges that were unimaginable in the past.
Through AI, the current Marketing landscape can be modelled to make educated predictions for the future, enabling Marketing teams to have accurate perspectives about where they can lead their Customer Experiences in the future.
In the near term, AI is still only as intelligent as the humans that program it. To date, human interaction and oversight is still crucial to mitigate for algorithmic bias, and also enhance Customer Experiences from a creative-curatorial standpoint.
The promise of AI to lead towards dynamic, almost instantaneously personalized consumer activations - where actions are analyzed in real time and treated as new inputs for market research - creates a compounding effect on ROI, opening up new opportunities for marketers.
Empowering your organisation with an AI-oriented approach can support you in effectively reaching your customers and building an increasingly personalised user experience.
Does your organization have access to a well-rounded mix of zero-, first-, and third-party data? Structured and unstructured?Is the data clean?
What BI platforms are you already using? Are they able to make your data interoperable and provide insights through predictive modelling, visualization, or recommendation?
Is there an internal willingness for to experiment and iterate? Is there precedent for nurturing creativity through unique combinations and applications of datasets?
Is your organization open to cross-functional collaboration of teams to harness the most power from AI capabilities?